{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import scipy.stats as stats\n",
    "import seaborn as sns\n",
    "from sklearn import preprocessing\n",
    "from sklearn.cross_validation import train_test_split\n",
    "import random\n",
    "import cPickle as pickle\n",
    "%matplotlib inline\n",
    "pd.set_option(\"display.max_colwidth\",100)\n",
    "sns.set(style=\"ticks\", color_codes=True)\n",
    "\n",
    "np.random.seed(1337)\n",
    "import keras\n",
    "\n",
    "from keras.preprocessing import sequence\n",
    "from keras.optimizers import RMSprop\n",
    "from keras.models import Sequential\n",
    "from keras.layers.core import Dense\n",
    "from keras.layers.core import Dropout\n",
    "from keras.layers.core import Activation\n",
    "from keras.layers.core import Flatten\n",
    "from keras.layers.convolutional import Convolution1D, MaxPooling1D\n",
    "from keras.constraints import maxnorm\n",
    "from keras.callbacks import ModelCheckpoint, EarlyStopping\n",
    "\n",
    "### Plotting settings ###\n",
    "plt.rcParams['font.weight'] = 'bold'\n",
    "plt.rcParams['axes.labelweight'] = 'bold'\n",
    "plt.rcParams['axes.labelpad'] = 5\n",
    "plt.rcParams['axes.linewidth']= 2.5\n",
    "plt.rcParams['xtick.labelsize']= 24\n",
    "plt.rcParams['ytick.labelsize']= 24\n",
    "plt.rcParams['axes.labelsize'] = 20\n",
    "plt.rcParams['figure.dpi'] = 200\n",
    "plt.rcParams['lines.linewidth'] = 7\n",
    "plt.rcParams['legend.markerscale'] = 1.5\n",
    "plt.rcParams['legend.fontsize'] = 20\n",
    "plt.rcParams['xtick.major.size'] = 12\n",
    "plt.rcParams['ytick.major.size'] = 12\n",
    "plt.rcParams['xtick.minor.size'] = 8\n",
    "plt.rcParams['ytick.minor.size'] = 8\n",
    "plt.rcParams['xtick.minor.width'] = 3\n",
    "plt.rcParams['ytick.minor.width'] = 3\n",
    "plt.rcParams['xtick.major.width'] = 4\n",
    "plt.rcParams['ytick.major.width'] = 4\n",
    "plt.rcParams['axes.facecolor'] = 'white'\n",
    "plt.rcParams['figure.autolayout'] = True\n",
    "plt.rcParams['mathtext.default'] = 'regular'\n",
    "plt.rcParams['xtick.color'] = 'black'\n",
    "plt.rcParams['ytick.color'] = 'black'\n",
    "plt.rcParams['axes.labelcolor'] = \"black\"\n",
    "plt.rcParams['axes.edgecolor'] = 'black'\n",
    "\n",
    "def train_model(x, y, border_mode='same', inp_len=50, nodes=40, layers=3, filter_len=8, nbr_filters=120,\n",
    "                dropout1=0, dropout2=0, dropout3=0, nb_epoch=3):\n",
    "    ''' Build model archicture and fit.'''\n",
    "    model = Sequential()\n",
    "    if layers >= 1:\n",
    "        model.add(Convolution1D(nb_filter=nbr_filters,filter_length=filter_len,input_dim=4,input_length=inp_len,border_mode=border_mode, activation='relu'))\n",
    "    if layers >= 2:\n",
    "        model.add(Convolution1D(nb_filter=nbr_filters,filter_length=filter_len,input_dim=4,input_length=inp_len,border_mode=border_mode, activation='relu'))\n",
    "        model.add(Dropout(dropout1))\n",
    "    if layers >= 3:\n",
    "        model.add(Convolution1D(nb_filter=nbr_filters,filter_length=filter_len,input_dim=4,input_length=inp_len,border_mode=border_mode, activation='relu'))\n",
    "        model.add(Dropout(dropout2))\n",
    "    model.add(Flatten())\n",
    "\n",
    "    model.add(Dense(nodes))\n",
    "    model.add(Activation('relu'))\n",
    "    model.add(Dropout(dropout3))\n",
    "    \n",
    "    model.add(Dense(1))\n",
    "    model.add(Activation('linear'))\n",
    "\n",
    "    #compile the model\n",
    "    adam = keras.optimizers.Adam(lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=1e-08)\n",
    "    model.compile(loss='mean_squared_error', optimizer=adam)\n",
    "\n",
    "    model.fit(x, y, batch_size=128, nb_epoch=nb_epoch, verbose=1)\n",
    "    return model\n",
    "\n",
    "def test_data(df, model, test_seq, obs_col, output_col='pred'):\n",
    "    scaler = preprocessing.StandardScaler()\n",
    "    scaler.fit(df[obs_col].reshape(-1,1))\n",
    "    #df.loc[:,'obs_stab'] = test_df['stab_df']\n",
    "    predictions = model.predict(test_seq).reshape(-1)\n",
    "    df.loc[:,output_col] = scaler.inverse_transform(predictions)\n",
    "    return df\n",
    "\n",
    "def one_hot_encode(df, col='utr', seq_len=50):\n",
    "    # Dictionary returning one-hot encoding of nucleotides. \n",
    "    nuc_d = {'a':[1,0,0,0],'c':[0,1,0,0],'g':[0,0,1,0],'t':[0,0,0,1], 'n':[0,0,0,0]}\n",
    "    \n",
    "    # Creat empty matrix.\n",
    "    vectors=np.empty([len(df),seq_len,4])\n",
    "    \n",
    "    # Iterate through UTRs and one-hot encode\n",
    "    for i,seq in enumerate(df[col].str[:seq_len]): \n",
    "        seq = seq.lower()\n",
    "        a = np.array([nuc_d[x] for x in seq])\n",
    "        vectors[i] = a\n",
    "    return vectors\n",
    "\n",
    "def r2(x,y):\n",
    "    slope, intercept, r_value, p_value, std_err = stats.linregress(data[x],data[y])\n",
    "    return r_value**2\n",
    "\n",
    "def eval_data(df, model, test_seq, obs_col, output_col='pred'):\n",
    "    scaler = preprocessing.StandardScaler()\n",
    "    scaler.fit(df[obs_col].reshape(-1,1))\n",
    "    #df.loc[:,'obs_stab'] = test_df['stab_df']\n",
    "    predictions = model.predict(test_seq).reshape(-1)\n",
    "    slope, intercept, r_value, p_value, std_err = stats.linregress(df[obs_col],scaler.inverse_transform(predictions))\n",
    "    return r_value**2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load all EGFP data. Rename total reads and RL columns to represent each data set in the merged dataframe.\n",
    "e1 & e2 = unmodified RNA EGFP........ep1 & ep2 = pseudouridine EGFP........emp1 & emp2 = m1pseudouridine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "e1 = pd.read_pickle('../../data/egfp_unmod_1.pkl')\n",
    "e1['e1_rl'] = e1['rl']\n",
    "e1['e1_total'] = e1['total_reads']\n",
    "e1['utr'] = e1['utr'].str[:50]\n",
    "\n",
    "e2 = pd.read_pickle('../../data/egfp_unmod_2.pkl')\n",
    "e2['e2_rl'] = e2['rl']\n",
    "e2['e2_total'] = e2['total']\n",
    "e2['utr'] = e2['utr'].str[:50]\n",
    "\n",
    "ep1 = pd.read_pickle('../../data/egfp_pseudo_1.pkl')\n",
    "ep1['ep1_rl'] = ep1['rl']\n",
    "ep1['ep1_total'] = ep1['total']\n",
    "ep1['utr'] = ep1['utr'].str[:50]\n",
    "\n",
    "ep2 = pd.read_pickle('../../data/egfp_pseudo_2.pkl')\n",
    "ep2['ep2_rl'] = ep2['rl']\n",
    "ep2['ep2_total'] = ep2['total']\n",
    "ep2['utr'] = ep2['utr'].str[:50]\n",
    "\n",
    "emp1 = pd.read_pickle('../../data/egfp_m1pseudo_1.pkl')\n",
    "emp1['emp1_rl'] = emp1['rl']\n",
    "emp1['emp1_total'] = emp1['total']\n",
    "emp1['utr'] = emp1['utr'].str[:50]\n",
    "                      \n",
    "emp2 = pd.read_pickle('../../data/egfp_m1pseudo_2.pkl')\n",
    "emp2['emp2_rl'] = emp2['rl']\n",
    "emp2['emp2_total'] = emp2['total']\n",
    "emp2['utr'] = emp2['utr'].str[:50]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Merge data into a single dataframe. Take the 'inner' merge to select UTRs that are found in all samples."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "320497\n",
      "320497\n",
      "318921\n",
      "318468\n",
      "318115\n"
     ]
    }
   ],
   "source": [
    "df = pd.merge(e1, e2, on='utr', how='inner')\n",
    "df = df[['utr', 'e1_rl', 'e2_rl', 'e1_total', 'e2_total']]\n",
    "\n",
    "df = pd.merge(df, ep1, on='utr', how='inner')\n",
    "df = df[['utr', 'e1_rl', 'e2_rl', 'e1_total', 'e2_total', 'ep1_total', 'ep1_rl']]\n",
    "print len(df)\n",
    "df = pd.merge(df, emp1, on='utr', how='inner')\n",
    "df = df[['utr', 'e1_rl', 'e2_rl', 'e1_total', 'e2_total', 'ep1_total', 'ep1_rl', 'emp1_total', 'emp1_rl']]\n",
    "print len(df)\n",
    "df = pd.merge(df, ep2, on='utr', how='inner')\n",
    "df = df[['utr', 'e1_rl', 'e2_rl', 'e1_total', 'e2_total', 'ep1_total', 'ep1_rl', 'emp1_total', 'emp1_rl',\n",
    "        'ep2_total', 'ep2_rl']]\n",
    "print len(df)\n",
    "df = pd.merge(df, emp2, on='utr', how='inner')\n",
    "df = df[['utr', 'e1_rl', 'e2_rl', 'e1_total', 'e2_total', 'ep1_total', 'ep1_rl', 'emp1_total', 'emp1_rl',\n",
    "        'ep2_total', 'ep2_rl', 'emp2_total', 'emp2_rl']]\n",
    "print len(df)\n",
    "\n",
    "df.dropna(how='any', inplace=True)\n",
    "print len(df)\n",
    "\n",
    "e1 = None\n",
    "e2 = None\n",
    "h8 = None\n",
    "h10 = None\n",
    "h8m = None\n",
    "h10m = None"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Enrich for UTRs that have high read counts across each data set. Sort by reads, remove 10k of lowest UTRs, move on to the next set, sort, cut 10k, etc.\n",
    "Note that the train / test split is the same between all data sets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df.sort_values('e2_total', inplace=True, ascending=False)\n",
    "df.reset_index(inplace=True, drop=True)\n",
    "df = df.iloc[:305000]\n",
    "df.sort_values('e1_total', inplace=True, ascending=False)\n",
    "df.reset_index(inplace=True, drop=True)\n",
    "df = df.iloc[:300000]\n",
    "df.sort_values('ep1_total', inplace=True, ascending=False)\n",
    "df.reset_index(inplace=True, drop=True)\n",
    "df = df.iloc[:290000]\n",
    "df.sort_values('emp1_total', inplace=True, ascending=False)\n",
    "df.reset_index(inplace=True, drop=True)\n",
    "df = df.iloc[:280000]\n",
    "df.sort_values('emp2_total', inplace=True, ascending=False)\n",
    "df.reset_index(inplace=True, drop=True)\n",
    "df = df.iloc[:270000]\n",
    "df.sort_values('ep2_total', inplace=True, ascending=False)\n",
    "df.reset_index(inplace=True, drop=True)\n",
    "df = df.iloc[:260000]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Typically, we do a train / test split based on total number of reads for a sample. Now we have multiple samples, splitting based on total reads for one say, egfp #1, may bias the model resulting in poorer performance on the other samples. In an attempt to get around that, we train-test split and model for each sample and then average the performances across all 6 models.\n",
    "Iterate through samples by change sort_values below. Do this for each sample and save the performance dictionary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df.sort_values('e1_total', inplace=True, ascending=False)\n",
    "df.reset_index(inplace=True, drop=True)\n",
    "\n",
    "# Train / test split --- test set is the 20k sequences with the highest number of reads\n",
    "e_test = df[:20000]\n",
    "e_test.reset_index(inplace=True, drop=True)\n",
    "e_train = df[20000:]\n",
    "e_train.reset_index(inplace=True, drop=True)\n",
    "\n",
    "# One-hot encode UTR sequences\n",
    "seq_e_train = one_hot_encode(e_train,seq_len=50)\n",
    "seq_e_test = one_hot_encode(e_test, seq_len=50)\n",
    "\n",
    "# Scale ribosome loads\n",
    "e_train.loc[:,'e1_scaled_rl'] = preprocessing.StandardScaler().fit_transform(e_train.loc[:,'e1_rl'].reshape(-1,1))\n",
    "e_train.loc[:,'e2_scaled_rl'] = preprocessing.StandardScaler().fit_transform(e_train.loc[:,'e2_rl'].reshape(-1,1))\n",
    "e_train.loc[:,'ep1_scaled_rl'] = preprocessing.StandardScaler().fit_transform(e_train.loc[:,'ep1_rl'].reshape(-1,1))\n",
    "e_train.loc[:,'ep2_scaled_rl'] = preprocessing.StandardScaler().fit_transform(e_train.loc[:,'ep2_rl'].reshape(-1,1))\n",
    "e_train.loc[:,'emp1_scaled_rl'] = preprocessing.StandardScaler().fit_transform(e_train.loc[:,'emp1_rl'].reshape(-1,1))\n",
    "e_train.loc[:,'emp2_scaled_rl'] = preprocessing.StandardScaler().fit_transform(e_train.loc[:,'emp2_rl'].reshape(-1,1))\n",
    "\n",
    "# Select only useful columns\n",
    "e_train = e_train[['utr', 'e1_scaled_rl', 'e1_total', 'e2_scaled_rl', 'e2_total', 'ep1_scaled_rl', 'ep1_total',\n",
    "                  'emp1_scaled_rl', 'emp1_total', 'ep2_scaled_rl', 'ep2_total', 'emp2_scaled_rl', 'emp2_total']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Train models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/3\n",
      "240000/240000 [==============================] - 59s - loss: 0.2281    \n",
      "Epoch 2/3\n",
      "240000/240000 [==============================] - 59s - loss: 0.1307    \n",
      "Epoch 3/3\n",
      "240000/240000 [==============================] - 59s - loss: 0.1185    \n",
      "e1_model (0.93503423517568884, 0.87653726230325768, 0.71397933362988242, 0.76100760981312088, 0.69952261553497475, 0.7747782315906393)\n",
      "\n",
      "Epoch 1/3\n",
      "240000/240000 [==============================] - 59s - loss: 0.3323    \n",
      "Epoch 2/3\n",
      "240000/240000 [==============================] - 59s - loss: 0.2117    \n",
      "Epoch 3/3\n",
      "240000/240000 [==============================] - 59s - loss: 0.1947    \n",
      "e2_model (0.89981547323098976, 0.89470861126621803, 0.70483378537896624, 0.77456589254386576, 0.6821694635277652, 0.78303647632264728)\n",
      "\n",
      "Epoch 1/3\n",
      "240000/240000 [==============================] - 59s - loss: 0.4986    \n",
      "Epoch 2/3\n",
      "240000/240000 [==============================] - 59s - loss: 0.4021    \n",
      "Epoch 3/3\n",
      "240000/240000 [==============================] - 59s - loss: 0.3750    \n",
      "ep1_model (0.79361209714635317, 0.75492456369978689, 0.78822608791295823, 0.81075037548724826, 0.7542563439230564, 0.76719143121753874)\n",
      "\n",
      "Epoch 1/3\n",
      "240000/240000 [==============================] - 59s - loss: 0.3855    \n",
      "Epoch 2/3\n",
      "240000/240000 [==============================] - 59s - loss: 0.2838    \n",
      "Epoch 3/3\n",
      "240000/240000 [==============================] - 59s - loss: 0.2679    \n",
      "ep2_model (0.81856308873662198, 0.79561131366435567, 0.78664722122707764, 0.8502538467476205, 0.74955367703814946, 0.80179346901472048)\n",
      "\n",
      "Epoch 1/3\n",
      "240000/240000 [==============================] - 59s - loss: 0.5197    \n",
      "Epoch 2/3\n",
      "240000/240000 [==============================] - 59s - loss: 0.4340    \n",
      "Epoch 3/3\n",
      "240000/240000 [==============================] - 59s - loss: 0.4131    \n",
      "emp1_model (0.78852253622724999, 0.73637711454329491, 0.77486504646240839, 0.7925161356339977, 0.76025210991887748, 0.7489941680301907)\n",
      "\n",
      "Epoch 1/3\n",
      "240000/240000 [==============================] - 59s - loss: 0.4183    \n",
      "Epoch 2/3\n",
      "240000/240000 [==============================] - 59s - loss: 0.3148    \n",
      "Epoch 3/3\n",
      "240000/240000 [==============================] - 59s - loss: 0.2889    \n",
      "emp2_model (0.84677308158906472, 0.82637086351675659, 0.75234800357591458, 0.80693477109987122, 0.7157442244932557, 0.82751004319778587)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Dictionaries to store models and model performance on all data\n",
    "model_dict = {}\n",
    "performance_dict = {}\n",
    "\n",
    "# Train & test on all samples\n",
    "for sample in ['e1', 'e2', 'ep1', 'ep2', 'emp1', 'emp2']:\n",
    "    m_name = sample + '_model'\n",
    "    \n",
    "    # Train\n",
    "    model_dict[m_name] = train_model(seq_e_train, e_train[sample + '_scaled_rl'], nb_epoch=3,border_mode='same',\n",
    "                       inp_len=50, nodes=40, layers=3, nbr_filters=120, filter_len=8, dropout1=0,\n",
    "                       dropout2=0,dropout3=0.2)\n",
    "    \n",
    "    # Save model\n",
    "    model = model_dict[m_name]\n",
    "    \n",
    "    # Evaluate on self and the other data sets\n",
    "    e1 = eval_data(e_test, model, seq_e_test, 'e1_rl')\n",
    "    e2 = eval_data(e_test, model, seq_e_test, 'e2_rl')\n",
    "    ep1 = eval_data(e_test, model, seq_e_test, 'ep1_rl')\n",
    "    ep2 = eval_data(e_test, model, seq_e_test, 'ep2_rl')\n",
    "    emp1 = eval_data(e_test, model, seq_e_test, 'emp1_rl')\n",
    "    emp2 = eval_data(e_test, model, seq_e_test, 'emp2_rl')\n",
    "    \n",
    "    # Save model performance\n",
    "    performance_dict[m_name] = (e1, e2, ep1, ep2, emp1, emp2)\n",
    "    print m_name, performance_dict[m_name]\n",
    "    print"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Save models and model performance on test sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "for k, v in model_dict.iteritems():\n",
    "    keras.models.save_model(v, k + '.h5')\n",
    "    \n",
    "with open('./saved_data/e1_model_performance.dict', 'wb') as f:\n",
    "    pickle.dump(performance_dict, f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Load performance dictionaries and average"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "avg_perf = np.zeros((6,6))\n",
    "for sample in ['e1', 'e2', 'ep1', 'ep2', 'emp1', 'emp2']:\n",
    "    d = pickle.load(open('./saved_data/' + sample + '_model_performance.dict', 'rb'))\n",
    "    a = np.array((d['e1_model'], d['e2_model'], d['ep1_model'], d['ep2_model'], d['emp1_model'], d['emp2_model']))\n",
    "    avg_perf = avg_perf + a\n",
    "    \n",
    "avg_perf = avg_perf / 6\n",
    "avg_perf = avg_perf.T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot heatmap of model performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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eeOIJGI1GANYnCVLPxr333qs4onD//v0VB7KTP5+ZkpKCqVOn2i3L2LFjTSc0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lGL8wrScfNJ2RiV1f6syKG3vaN7s3y7vFX8zkmxBc9fllmwp1E6rv2kgcmyce0U2dljs7D32bOw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\npU+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lKZFeIp44sl9kIKZxKItErwjphI2O5QHXTZq5/LBMHhgR61weyFYivVQ8zKVfRACm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\nsVJjfGWtAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3ab537cb90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax =sns.heatmap(avg_perf, annot=True, cmap='YlOrRd', vmin=0.5, vmax=1, annot_kws={'size':'20', 'color':'black'}, cbar=False, square=True)\n",
    "f = ax.get_figure()\n",
    "f.set_size_inches((9.5,9.5))\n",
    "ax.set_xticklabels([r'U${_E}{_1}$', r'U${_E}{_2}$', r'$\\Psi{_1}$', r'$\\Psi{_2}$', r'$m^1\\Psi{_1}$',\n",
    "                    r'$m^1\\Psi{_2}$'], fontsize=29, fontdict={'family':'arial'})\n",
    "ax.set_yticklabels([r'$m^1\\Psi{_2}$', r'$m^1\\Psi{_1}$', r'$\\Psi{_2}$', r'$\\Psi{_1}$', r'U${_E}{_2}$',\n",
    "                    r'U${_E}{_1}$'], fontsize=29, fontdict={'family':'arial'}, rotation=0)\n",
    "ax.set_xlabel('Model Training Data', size = 35, fontdict={'family':'arial'})\n",
    "ax.set_ylabel('Model Test Data', size=35, fontdict={'family':'arial'})\n",
    "ax.yaxis.set_ticks_position('left')\n",
    "ax.xaxis.set_ticks_position('top')\n",
    "ax.xaxis.set_label_position('top');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "f.savefig('/data/5utr/modeling/combined_data/chemistry/images/egfp_chemistries_only_avg_perf_6models_tested_heatmap_201709256.eps', format=('eps'))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
